AMCE calculates the average marginal component effects from a BART-estimated conjoint model.
AMCE(
data,
model,
attribs,
ref_levels,
method = "bayes",
alpha = 0.05,
cores = 1,
skip_checks = FALSE
)AMCE returns an object of type "cjbart", a list object.
A data.frame containing the average marginal component effects
The significance level used to compute the credible interval
A data.frame, containing all attributes, covariates, the outcome and id variables to analyze.
A model object, the result of running cjbart()
Vector of attribute names for which IMCEs will be predicted
Vector of reference levels, used to calculate marginal effects
Character string, setting the variance estimation method to use. When method is "parametric", a typical combined variance estimate is employed; when method = "bayes", the 95% posterior interval is calculated; and when method = "rubin", combination rules are used to combine the variance analogous to in multiple imputation analysis.
Number between 0 and 1 -- the significance level used to compute confidence/posterior intervals. When method = "bayes", the posterior interval is calculated by taking the alpha/2 and (1-alpha/2) quantiles of the posterior draws. When method = "rubin", the confidence interval equals the IMCE +/- qnorm(alpha/2). By default, alpha is 0.05 i.e. generating a 95% confidence/posterior interval.
Number of CPU cores used during prediction phase
Boolean, indicating whether to check the structure of the data (default = FALSE). Only set this to TRUE if you are confident that the data is structured appropriately
The AMCE estimates are the average of all computed OMCEs.
cjbart()